An Efficient Demand Side Management System with a New Optimized Home Energy Management Controller in Smart Grid

Hussain, Hafiz Majid; Javaid, Nadeem; Iqbal, Sohail; Hasan, Qadeer Ul; Aurangzeb, Khursheed; Alhussein, Musaed

doi:10.3390/en11010190

Cited by 147 publications

(95 citation statements)

References 30 publications

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“…However, they should follow the context, for instance, smart light systems in each room will be switched on when users move in, and turned off when they leave, or the clothes dryer should be operated after the washing machine completes its operation, and so on. Moreover, we adopt a CPP tariff [29] for comparative analysis to show the effectiveness of our approach. Hence, we are able to determine the peak load and off-peak load of each time horizon

T

based on CPP signal (threshold load balancing values).…”

Section: Simulation and Discussionmentioning

confidence: 99%

Consensual Negotiation-Based Decision Making for Connected Appliances in Smart Home Management Systems

Bui

Jung

Camacho

2018

Sensors

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Recently, the concept of Internet of Agent has been introduced as a potential technology that pushes intelligence, data processing, analytics and communication capabilities down to the point where the data originates. In this paper, we introduce a novel approach for a Decentralized Home Energy Management System by applying the Internet of Agent concept. In particular, we first present an Internet of Agent framework in terms of sensing, communicating and collaborating among connected appliances. Then, the decentralized management based on consensual negotiation mechanism with several intelligent techniques are proposed for dynamic scheduling connected appliance. Specifically, by applying the Internet of Agent framework, connected appliances are regarded as smart agents that are able to make individual decisions by reaching agreement over the exchange of operations on competitive resources. Furthermore, in this study, the load balancing problem in which load shifting is able to reduce the electricity demand during peak hours is taken into account in order to emphasize the effectiveness of our approach. For the experiment, we develop a simulation of smart home environment to evaluate our approach using NetLogo, a tool which provides real-time analysis in the modeling and simulation domain of complex systems.

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T

based on CPP signal (threshold load balancing values).…”

Section: Simulation and Discussionmentioning

confidence: 99%

Consensual Negotiation-Based Decision Making for Connected Appliances in Smart Home Management Systems

Bui

Jung

Camacho

2018

Sensors

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“…In addition, it reduces electricity cost and the consumer's comfort is protected. In order to show a higher search efficiency and dynamic capability to achieve an optimal solution, Hafiz et al [13] present an efficient HEMS (EHEMS) based on a genetic harmony search algorithm (GHSA). This proposed method reduces the electricity cost, peak-to-average ratio (PAR) and maximizes the consumer comfort via the real time electricity pricing (RTEP) and CPP.…”

Section: Related Workmentioning

confidence: 99%

“…Algorithm 1 presents the optimal scheduling technique. The execution steps of the proposed optimal scheduling techniques depend on the complexity at lines (8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24) in the for-loop. The complexities of other lines (i.e., 10-22) are more than these lines.…”

Section: Algorithm 1: Proposed Optimal Scheduling Technique [26]mentioning

confidence: 99%

An Efficient Power Scheduling in Smart Homes Using Jaya Based Optimization with Time-of-Use and Critical Peak Pricing Schemes

Dahunsi

Javaid

et al. 2018

Energies

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Presently, the advancements in the electric system, smart meters, and implementation of renewable energy sources (RES) have yielded extensive changes to the current power grid. This technological innovation in the power grid enhances the generation of electricity to meet the demands of industrial, commercial and residential sectors. However, the industrial sectors are the focus of power grid and its demand-side management (DSM) activities. Neglecting other sectors in the DSM activities can deteriorate the total performance of the power grid. Hence, the notion of DSM and demand response by way of the residential sector makes the smart grid preferable to the current power grid. In this circumstance, this paper proposes a home energy management system (HEMS) that considered the residential sector in DSM activities and the integration of RES and energy storage system (ESS). The proposed HEMS reduces the electricity cost through scheduling of household appliances and ESS in response to the time-of-use (ToU) and critical peak price (CPP) of the electricity market. The proposed HEMS is implemented using the Earliglow based algorithm. For comparative analysis, the simulation results of the proposed method are compared with other methods: Jaya algorithm, enhanced differential evolution and strawberry algorithm. The simulation results of Earliglow based optimization method show that the integration of RES and ESS can provide electricity cost savings up to 62.80% and 20.89% for CPP and ToU. In addition, electricity cost reduction up to 43.25% and 13.83% under the CPP and ToU market prices, respectively.

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“…Smart grids (SGs) that attract intensive attention are reliable power grids having self-healing ability, comprising the consumers producing their own energy based on renewable energy sources with minimum cost and allowing the use of available infrastructure with full capacity [1][2][3][4]. In addition, SGs give the opportunity for consumers to operate demand response programs independently so that the optimal matching between source and load can be spread to each region of the horizontal time axis by considering consumer habits.…”

Section: Introductionmentioning

confidence: 99%

The Determination of Load Profiles and Power Consumptions of Home Appliances

Issi

Kaplan

2018

Energies

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Abstract:In recent years, the increment of distributed electricity generation based on renewable energy sources and improvement of communication technologies have caused the development of next-generation power grids known as smart grids. The structures of smart grids have bidirectional communication capability and enable the connection of energy generated from distributed sources to any point on the grid. They also support consumers in energy efficiency by creating opportunities for management of power consumption. The information on power consumption and load profiles of home appliances is essential to perform load management in the dwelling accurately. In this study, the power consumption data for all the basic home appliances, utilized in a two-person family in Çankırı, Turkey, was obtained with high resolution in one-second intervals. The detailed power consumption analysis and load profile were executed for each home appliance. The obtained data is not only the average power consumption of each appliance but also characterizes different operating modes or their cycles. In addition, the impact of these devices on home energy management studies and their standby power consumptions were also discussed. The acquired data is an important source to determine the load profile of individual home appliances precisely in home energy management studies. Although the results of this study do not completely reflect the energy consumption behavior of the people who live in this region, they can reveal the trends in load demands based on a real sample and customer consumption behavior of a typical two-person family.

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An Efficient Demand Side Management System with a New Optimized Home Energy Management Controller in Smart Grid

Cited by 147 publications

References 30 publications

Consensual Negotiation-Based Decision Making for Connected Appliances in Smart Home Management Systems

Consensual Negotiation-Based Decision Making for Connected Appliances in Smart Home Management Systems

An Efficient Power Scheduling in Smart Homes Using Jaya Based Optimization with Time-of-Use and Critical Peak Pricing Schemes

The Determination of Load Profiles and Power Consumptions of Home Appliances

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